{"title":"利用回归分析关键宏观经济变量对BSE的依赖性","authors":"B. K. Som, Himanshu Goel","doi":"10.4018/ijabe.308782","DOIUrl":null,"url":null,"abstract":"This paper aims to analyze the dependence of key macroeconomic variables on Bombay Stock Exchange (BSE) Sensex using regression modelling technique in R-studio. Monthly data points spanning a period of last years from 2012 to 2019 has been used for the empirical investigation. The results of the model indicate that Long Term Interest Rate (LTINT), Consumer Price Index (CPI) and Morgan Stanley Capital International (MSCI) are found to be significant while Index of Industrial Production (IIP) and Foreign Exchange (FX) are insignificant. Also, the value or r-square indicates that 93 percent of the variation in the dependent variable is explained by the selected Independent variables. Also, the dataset was checked for normality and linearity using appropriate graphs. The results of this paper will be of immense use for the investors in predicting the stock price movement.","PeriodicalId":41154,"journal":{"name":"International Journal of Applied Behavioral Economics","volume":" ","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing Dependence of Key Macroeconomic Variables on BSE Using Regression\",\"authors\":\"B. K. Som, Himanshu Goel\",\"doi\":\"10.4018/ijabe.308782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to analyze the dependence of key macroeconomic variables on Bombay Stock Exchange (BSE) Sensex using regression modelling technique in R-studio. Monthly data points spanning a period of last years from 2012 to 2019 has been used for the empirical investigation. The results of the model indicate that Long Term Interest Rate (LTINT), Consumer Price Index (CPI) and Morgan Stanley Capital International (MSCI) are found to be significant while Index of Industrial Production (IIP) and Foreign Exchange (FX) are insignificant. Also, the value or r-square indicates that 93 percent of the variation in the dependent variable is explained by the selected Independent variables. Also, the dataset was checked for normality and linearity using appropriate graphs. The results of this paper will be of immense use for the investors in predicting the stock price movement.\",\"PeriodicalId\":41154,\"journal\":{\"name\":\"International Journal of Applied Behavioral Economics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Behavioral Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijabe.308782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Behavioral Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijabe.308782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Analyzing Dependence of Key Macroeconomic Variables on BSE Using Regression
This paper aims to analyze the dependence of key macroeconomic variables on Bombay Stock Exchange (BSE) Sensex using regression modelling technique in R-studio. Monthly data points spanning a period of last years from 2012 to 2019 has been used for the empirical investigation. The results of the model indicate that Long Term Interest Rate (LTINT), Consumer Price Index (CPI) and Morgan Stanley Capital International (MSCI) are found to be significant while Index of Industrial Production (IIP) and Foreign Exchange (FX) are insignificant. Also, the value or r-square indicates that 93 percent of the variation in the dependent variable is explained by the selected Independent variables. Also, the dataset was checked for normality and linearity using appropriate graphs. The results of this paper will be of immense use for the investors in predicting the stock price movement.